检索结果-南通市图书馆

natural language processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with natural language processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops anatural language processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： natural language processing information retrieval image captioning deep learning metaheuristics

natural language processing with Optimal Deep Learning Based Fake News Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 3529-3544页

作者： Sara AAlthubiti Fayadh Alenezi Romany F.Mansour Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make manipulated societal insights.The spread of low-quality news in social networking sites has a negative influence upon people as well as the society.In order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)methods.The latest advancements in Deep Learning(DL)models and complex natural language processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of natural language processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)model.The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner.In the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word embedding.Besides,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake news.Finally,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal manner.The novelty of the study lies in the design of STO algorithm with DBN model for FND.In order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark datasets.The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.

关键词： natural language processing text mining fake news detection deep belief network machine learning evolutionary algorithm

Literature classification and its applications in condensed matter physics and materials science by natural language processing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2024年第5期33卷 117-123页

作者：吴思远朱天念涂思佳肖睿娟袁洁吴泉生李泓翁红明 Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100190China College of Materials Science and Optoelectronic Technology University of Chinese Academy of SciencesBeijing 100049China Condensed Matter Physics Data Center of Chinese Academy of Sciences Beijing 100190China

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

关键词： natural language processing text mining materials science

Paradigm Shift in natural language processing

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Machine Intelligence Research 2022年第3期19卷 169-183页

作者： Tian-Xiang Sun Xiang-Yang Liu Xi-Peng Qiu Xuan-Jing Huang School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Intelligent Information processing Fudan UniversityShanghai 200438China

In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.

关键词： natural language processing pre-trained language models deep learning sequence-to-sequence paradigm shift

Numerical‐discrete‐scheme‐incorporated recurrent neural network for tasks in natural language processing

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1415-1424页

作者： Mei Liu Wendi Luo Zangtai Cai Xiujuan Du Jiliang Zhang Shuai Li School of Information Science and Engineering Lanzhou UniversityLanzhouChina The State Key Laboratory of Tibetan Intelligent Information processing and Application Qinghai Normal UniversityXiningChina Department of Electronic and Electrical Engineering The University of SheffieldSheffieldUK

A variety of neural networks have been presented to deal with issues in deep learning in the last decades.Despite the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive resources.Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations.This connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical analysis.Simple RNN is regarded as a discretisation of the forward Euler scheme.Considering the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its guidance.Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks.The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.

关键词： deep learning natural language processing neural network text analysis

The State of the Art of natural language processing-A Systematic Automated Review of NLP Literature Using NLP Techniques

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Data Intelligence 2023年第3期5卷 707-749页

作者： Jan Sawicki Maria Ganzha Marcin Paprzycki Warsaw University of Technology Polish Academy of Sciences

Nowadays,natural language processing(NLP)is one of the most popular areas of,broadly understood,artificial intelligence.Therefore,every day,new research contributions are posted,for instance,to the arXiv repository.Hence,it is rather difficult to capture the current"state of the field"and thus,to enter it.This brought the id-art NLP techniques to analyse the NLP-focused literature.As a result,(1)meta-level knowledge,concerning the current state of NLP has been captured,and(2)a guide to use of basic NLP tools is provided.It should be noted that all the tools and the dataset described in this contribution are publicly available.Furthermore,the originality of this review lies in its full automation.This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

关键词： natural language processing Text processing Literature survey Keyword search Keyphrase search Text embeddings Text summarizations

Deep Learning with natural language processing Enabled Sentimental Analysis on Sarcasm Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第3期44卷 2553-2567页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl HofufAl-Ahsa31982Saudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

关键词： Sentiment analysis sarcasm detection deep learning natural language processing n-grams hyperparameter tuning

Prediction of Academic Performance of Students in Online Live Classroom Interactions-An Analysis Using natural language processing and Deep Learning Methods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Social Computing 2023年第1期4卷 12-29页

作者： Yuanyi Zhen Jar-Der Luo Hui Chen 不详 School of Chinese language and Literature Beijing Foreign Studies UniversityBeijing 100089China

Prior studies have shown the importance of classroom dialogue in academic performance,through which knowledge construction and social interaction among students take place.However,most of them were based on small scale or qualitative data,and few has explored the availability and potential of big data collected from online classrooms.To address this issue,this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques.The features of interactive types and emotional expression are extracted from classroom dialogues.We then develop neural network models based on these features to predict high-and low-academic performing students,and employ interpretable AI(artificial intelligence)techniques to determine the most important predictors in the prediction models.In both STEM(science,technology,engineering,mathematics)and non-STEM courses,it is found that high-performing students consistently exhibit more positive emotion,cognition and off-topic dialogues in all stages of the lesson than low-performing students.However,while the metacognitive dialogue illustrates its importance in non-STEM courses,this effect cannot be found in STEM courses.While high-performing students in non-STEM courses show negative emotion in the last stage of lessons,STEM students show positive emotion.

关键词： academic performance prediction live classroom dialogue emotional expression interactive type natural language processing deep learning

Automating Transfer Credit Assessment-A natural language processing-Based Approach

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 2257-2274页

作者： Dhivya Chandrasekaran Vijay Mago Lakehead University Thunder BayOntarioP7B 5E1Canada

Student mobility or academic mobility involves students moving between institutions during their post-secondary education,and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student.In general,this process involves domain experts comparing the learning outcomes of the courses,to decide on offering transfer credits to the incoming students.This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity.The proposed research article focuses on identifying a model that exploits the advancements in the field of natural language processing(NLP)to effectively automate this process.Given the unique structure,domain specificity,and complexity of learning outcomes(LOs),a need for designing a tailor-made model arises.The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.The similarity between LOs is further aggregated to form course to course similarity.Due to the lack of quality benchmark datasets,a new benchmark dataset containing seven course-to-course similarity measures is proposed.Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency.While providing an efficient model to assess the similarity between courses with existing resources,this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.

关键词： Articulation agreements higher education natural language processing semantic similarity